Abstract: Systems and methods that use computer vision techniques in connection with robotic surgery are discussed. A robotic surgery system may include an implantable device engagement sub-system, a targeting sub-system, and/or an insertion verification sub-system. The system may use computer vision techniques to facilitate implanting a micro-manufactured bio-compatible electrode device in biological tissue (e.g., neurological tissue such as the brain) using robotic assemblies. The system can attach, via robotic manipulation, the electrode to an engagement element of an insertion needle.

Abstract: Systems and methods that use computer vision techniques in connection with robotic surgery are discussed. A robotic surgery system may include an implantable device engagement sub-system, a targeting sub-system, and/or an insertion verification sub-system. The system may use computer vision techniques to facilitate implanting a micro-manufactured bio-compatible electrode device in biological tissue (e.g., neurological tissue such as the brain) using robotic assemblies. The system can attach, via robotic manipulation, the electrode to an engagement element of an insertion needle.

Abstract: Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.

Abstract: Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.